Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study

Background: To explore the ability of the deep learning network Inception-v3 to differentiate between papillary thyroid carcinomas (PTCs) and benign nodules in ultrasound images. Methods: A total of 2,836 thyroid ultrasound images from 2,235 patients were divided into a training dataset and a test dataset. Inception-v3 was trained and tested to crop the margin of the images of nodules and provide a differential diagnosis. The sizes and sonographic features of nodules were further analysed to identify the factors that may influence diagnostic efficiency. Statistical analyses included χ 2 and Fisher’s exact tests and univariate and multivariate analyses. Results: There were 1,275 PTCs and 1,162 benign nodules in the training group and 209 PTCs and 190 benign nodules in the test group. A margin size of 50 pixels and an input size of 384×384 showed the best outcome after training, and these parameters were selected for the test group. In the test group, the sensitivity and specificity for Inception-v3 were 93.3% (195/209) and 87.4% (166/190), respectively. Inception-v3 displayed the highest accuracy for 0.5–1.0 cm nodules. The accuracy differed according to the margin description (P=0.024). Taller nodules were more accurately diagnosed than were wider nodules (P=0.015). Microcalcification [odds ratio (OR) =0.254, 95% confidence interval (CI): 0.076–0.847, P=0.026] and taller shape (OR =0.243, 95% CI: 0.073–0.810, P=0.021) were negatively associated with misdiagnosis rate. Conclusions: Inception-v3 can achieve an excellent diagnostic efficiency. Nodules that are 0.5–1.0 cm in size and have microcalcification and a taller shape can be more accurately diagnosed by Inception-v3.